Instructions to use textattack/roberta-base-WNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/roberta-base-WNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/roberta-base-WNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-WNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-WNLI") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99ef5239a286edce6918ed0fcfc996fb48ed8515214d2370167c9da2dd390a03
- Size of remote file:
- 501 MB
- SHA256:
- 70e4b71fc11590cfefe37c00ca768533c846b45f9e4d823805e699f67f1e0324
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.